{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic modeling with PyMC3\n",
    "\n",
    "This article is an introduction to [topic models](https://en.wikipedia.org/wiki/Topic_model) and their naive implementation with [PyMC3](https://docs.pymc.io/). It is mainly targeted at readers with little or no prior PyMC3 experience. The examples focus on simplicity rather than efficiency and I'll provide links to more efficient implementations at the end of the article. I'll also show how formal definitions of topic models relate to PyMC3 code. For this article it is helpful to have a basic understanding of probability theory. \n",
    "\n",
    "PyMC3 is an open source probabilistic programming library. It allows the specification of Bayesian statistical models with an intuitive syntax on an abstraction level similar to that of their formal definitions and [plate notations](https://en.wikipedia.org/wiki/Plate_notation). PyMC3 does automatic [Bayesian inference](https://en.wikipedia.org/wiki/Bayesian_inference) for unknown variables in probabilistic models via [Markow Chain Monte Carlo](https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo) (MCMC) sampling or via [variational inference](https://en.wikipedia.org/wiki/Variational_Bayesian_methods) (ADVI, SVGD, ...). \n",
    "\n",
    "In this article MCMC is used to obtain posterior estimates for unknown model variables. I won't introduce PyMC3 from scratch here and therefore recommend to read the initial sections of the PyMC3 [getting started guide](https://docs.pymc.io/notebooks/getting_started.html) first (up to and including the linear regression example). But even without reading it you should be able to follow this article and get an intuition how PyMC3 can be used to implement topic models. The following implementation examples are based on PyMC3 version 3.5.\n",
    "\n",
    "## Topic models\n",
    "\n",
    "Topic models are statistical models for discovering abstract topics in a collection of documents. For example, a document containing words like \"dog\", \"cat\" or \"rat\" likely has a different underlying topic than a document containing words like \"CPU\", \"GPU\" or \"RAM\". When learning a topic model only the words in documents are observed, the topics to be discovered are not observed. The random variables that model topics are therefore called hidden or latent variables and the corresponding model is called a latent variable model. When learning a latent variable model, the latent variables are inferred together with the other unknown model parameters.\n",
    "\n",
    "The topic models presented in this article are [generative models](https://en.wikipedia.org/wiki/Generative_model). These models learn a joint probability distribution $p(x, z) = p(x \\lvert z)p(z)$ over observed words $x$ in a training dataset and hidden topics $z$. By sampling from this distribution new documents can be generated from their underlying topic(s), hence the term *generative model*. This is in contrast to a *discriminative model* that only learns a probability distribution $p(z \\lvert x)$ over topics given a word $x$ or a set of words, often used in a supervised learning context where topics (classes) are observed.\n",
    "\n",
    "Throughout the following examples an over-simplified set of `documents` is used. The documents contain words that we can categorize into topics *programming languages*, *machine learning* and *databases*. During inference though only abstract topics `0`, `1`, `2`, ... are assigned to documents and words, semantic interpretation is up to us. For all presented models, the number of topics $K$ is pre-defined based on our intuition and is not inferred. For processing documents with PyMC3 they are categorically encoded based on the entries in a `vocabulary`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "documents = [['Python', 'Scala', 'Python', 'Python', 'Java'],\n",
    "             ['Scala', 'Python', 'Python', 'Java', 'Scala'],\n",
    "             ['Python', 'Python', 'Scala', 'Python'],\n",
    "             ['Java', 'Java', 'Java', 'Scala', 'Scala'],\n",
    "             ['Scala', 'Scala', 'Scala', 'Python', 'Java', 'Scala', 'deep learning'],\n",
    "             ['Python', 'Scala', 'Python', 'Python', 'Python', 'machine learning'],\n",
    "             ['Java', 'Python', 'Python', 'Java', 'Scala'],\n",
    "             ['deep learning', 'statistics', 'machine learning', 'Python'],\n",
    "             ['machine learning', 'machine learning', 'deep learning', 'deep learning', 'machine learning'],\n",
    "             ['statistics', 'Python', 'statistics', 'statistics', 'deep learning', 'Postgres'],\n",
    "             ['deep learning', 'machine learning', 'machine learning', 'deep learning', 'deep learning', 'Postgres'],\n",
    "             ['MySQL', 'Cassandra', 'Postgres', 'Postgres', 'Postgres', 'machine learning'],\n",
    "             ['Cassandra', 'Cassandra', 'Postgres', 'Scala', 'MySQL', 'MySQL']]\n",
    "\n",
    "# Number of topics\n",
    "K = 3\n",
    "\n",
    "# Number of documents \n",
    "D = len(documents)\n",
    "\n",
    "# (Ab)use label encoder for categorical encoding of words\n",
    "encoder = LabelEncoder()\n",
    "encoder.fit([word for document in documents for word in document])\n",
    "\n",
    "# Vocabulary derived from documents\n",
    "vocabulary = encoder.classes_\n",
    "\n",
    "# Vocabulary size \n",
    "V = len(vocabulary)\n",
    "\n",
    "# Encoded documents\n",
    "X = [encoder.transform(d) for d in documents]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Bayes model\n",
    "\n",
    "We start with a simple topic model where each document is assigned a single topic based on the words contained in that document i.e. documents are classified into different topics. We further assume that the occurence of one word in a document doesn't give any additional information about the occurence of other words in that document. The distribution of words in a document only depends on the topic of that document. This means that the words in a document are conditionally independent i.e. independent given the document's topic. Models that make this naive assumption are called *Naive Bayes* models. \n",
    "\n",
    "The formal definition of the model is\n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "\\boldsymbol{\\theta} &\\sim \\mathrm{Dir(\\boldsymbol\\alpha)} \\\\\n",
    "\\boldsymbol{\\phi}_{k=1,\\ldots,K} &\\sim \\mathrm{Dir(\\boldsymbol\\beta)} \\\\\n",
    "z_{d=1,\\ldots,D} &\\sim \\mathrm{Cat}(\\boldsymbol{\\theta}) \\\\\n",
    "w_{d=1,\\ldots,D,n=1,\\ldots,N_d} &\\sim \\mathrm{Cat}(\\boldsymbol{\\phi}_{z_{d}})\n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "where \n",
    "\n",
    "- $\\boldsymbol\\theta$ is a random variable that models the global topic distribution. It is a $K$-dimensional random vector whose elements are the global topic probabilities. They must be non-negative and sum up to 1. A prior distribution that enforces these constraints is the [Dirichlet distribution](https://en.wikipedia.org/wiki/Dirichlet_distribution). It is parameterized with hyper-parameter $\\boldsymbol\\alpha$. We start with a uniform prior and therefore set $\\boldsymbol\\alpha = \\boldsymbol{1}$.\n",
    "\n",
    "- $\\boldsymbol\\phi_k$ is a random variable that models the word distribution of topic $k$. It is a $V$-dimensional vector whose elements are the probabilities of vocabulary words where $V$ is the size of the vocabulary. The prior is also a Dirichlet distribution but with hyper-parameter $\\boldsymbol\\beta$. Our assumption is that only a small set of words from the vocabulary have higher probability per topic. We therefore define a sparse Dirichlet prior by setting the elements of $\\boldsymbol\\beta$ to values $<1$.\n",
    "\n",
    "- $z_d$ is the topic of document $d$. It follows a [categorical distribution](https://en.wikipedia.org/wiki/Categorical_distribution) that is parameterized by $\\boldsymbol\\theta$.\n",
    "\n",
    "- $w_{dn}$ is the $n$-th word of document $d$. It also follows a categorical distribution that is parameterized by $\\boldsymbol\\phi_{z_{d}}$ i.e. the word distribution of topic $z_d$.\n",
    "\n",
    "From a generative model perspective, a document's words can be generated by first sampling a topic from the global topic distribution and then sampling the words from that topic's word distribution. For drawing the words of a document we could also use a [multinomial distribution](https://en.wikipedia.org/wiki/Multinomial_distribution) but for easier comparison with the model in the next section we use a categorical distribution. The model can be summarized with the following plate notation:\n",
    "\n",
    "![nbm](images/topic-models/nbm.png?v2)\n",
    "\n",
    "Model specification with PyMC3 closely follows its formal definition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pymc3 as pm\n",
    "\n",
    "# Hyper-parameter for uniform Dirichlet prior\n",
    "alpha = np.ones(K)\n",
    "\n",
    "# Hyper-parameter for sparse Dirichlet prior\n",
    "beta = np.ones(V) * 0.3\n",
    "\n",
    "with pm.Model() as model:\n",
    "    # Global topic distribution\n",
    "    theta = pm.Dirichlet('theta', a=alpha)\n",
    "    \n",
    "    # Word distributions for K topics\n",
    "    phi = pm.Dirichlet('phi', a=beta, shape=(K, V))\n",
    "    \n",
    "    # Topic of documents\n",
    "    z = pm.Categorical('z', p=theta, shape=(D,))\n",
    "    \n",
    "    for i in range(D):\n",
    "        # Words of document\n",
    "        w = pm.Categorical(f'w_{i}', p=phi[z[i]], observed=X[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Variables $\\boldsymbol\\phi_k$ from the formal definition are implemented as PyMC3 random variable `phi` with shape `(K, V)`. The variable name is the first argument to the distribution constructor.\n",
    "\n",
    "- Variables $z_d$ are implemented as PyMC3 random variable `z` with shape `(D,)` where $D$ is the number of documents (13 in our example). \n",
    "\n",
    "- Variables $w_{dn}$ are implemented as PyMC3 random variables `w_0`, `w_1`, ..., `w_12`, one for each document. These variables are observed and linked to training data via the `observed` parameter. These variables are not changed during model fitting. Their shape is derived from the number of words in a document.\n",
    "\n",
    "Next step is to infer the global topic distribution, the word distribution for each topic and the topic for each document. The corresponding estimates for `theta`, `phi` and `z` are computed from posterior samples obtained via MCMC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sequential sampling (1 chains in 1 job)\n",
      "CompoundStep\n",
      ">NUTS: [phi, theta]\n",
      ">CategoricalGibbsMetropolis: [z]\n",
      "100%|██████████| 2500/2500 [00:21<00:00, 118.32it/s]\n",
      "There were 30 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "Only one chain was sampled, this makes it impossible to run some convergence checks\n"
     ]
    }
   ],
   "source": [
    "with model:    \n",
    "    trace = pm.sample(2000, chains=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `trace` object returned by `pm.sample` contains `2000` posterior samples for variables `theta`, `phi` and `z`. For visualizing `theta` posterior estimates we use the built-in `pm.plot_posterior` function. It plots the mean and the 95% highest posterior density (HPD) interval for each element of random vector `theta` i.e. `theta_0`, `theta_1` and `theta_2`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 864x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pm.plot_posterior(trace, varnames=['theta']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The posterior means closely match the relative frequency or distribution of topics in our dataset of 13 documents: 7 documents are related to programming languages, 4 documents are related to machine learning and 2 documents are related to databases. \n",
    "\n",
    "For visualizing the prominent words of each topic we plot [kernel density estimates](https://en.wikipedia.org/wiki/Kernel_density_estimation) for each element of `phi`. We can see that for topic `0` the words with the highest probabilities are \"Python\", \"Scala\" and \"Java\", for topic `1` these are \"deep learning\", \"machine learning\" and \"statistics\" and for topic `2` \"Postgres\", \"MySQL\" and \"Cassandra\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "def plot_phi_estimates(trace):\n",
    "    plt.figure(figsize=(20,5))\n",
    "    for i in range(K):\n",
    "        plt.subplot(1, 3, i+1)\n",
    "        for j in range(V):\n",
    "            sns.distplot(trace['phi'][:, i, j], kde=True, hist=False, label=vocabulary[j])\n",
    "            plt.ylim([0, 10])\n",
    "        plt.title(f'Phi density estimates for topic {i}')\n",
    "        plt.legend(vocabulary)\n",
    "        \n",
    "plot_phi_estimates(trace)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For obtaining the topic of each document we compute the posterior mode of `z` but omit computing uncertainty estimates here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic of document  0 = 0\n",
      "Topic of document  1 = 0\n",
      "Topic of document  2 = 0\n",
      "Topic of document  3 = 0\n",
      "Topic of document  4 = 0\n",
      "Topic of document  5 = 0\n",
      "Topic of document  6 = 0\n",
      "Topic of document  7 = 1\n",
      "Topic of document  8 = 1\n",
      "Topic of document  9 = 1\n",
      "Topic of document 10 = 1\n",
      "Topic of document 11 = 2\n",
      "Topic of document 12 = 2\n"
     ]
    }
   ],
   "source": [
    "for i in range(D):\n",
    "    topics, counts = np.unique(trace['z'][:, i], return_counts=True)\n",
    "    print(f'Topic of document {i: >2} = {topics[np.argmax(counts)]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Latent Dirichlet allocation\n",
    "\n",
    "Making the assumption that a document has only a single underlying topic is often too restrictive. A more realistic assumption is that a document is made up of a mixture of topics. Instead of having a global topic distribution we want to have a topic distribution per document. By additionally allowing the assignment of topics to individual words we end up with a topic model called [Latent Dirichlet allocation](https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation) (LDA). It is a popular topic model that can be summarized with the following plate notation:\n",
    "\n",
    "![lda](images/topic-models/lda.png?v2)\n",
    "\n",
    "The difference to the Naive Bayes model is that the $D$ and $N_d$ plates are extended by one node to the left. The LDA model can be formally described as \n",
    "\n",
    "$$\n",
    "\\begin{align*}\n",
    "\\boldsymbol{\\theta}_{d=1,\\ldots,D} &\\sim \\mathrm{Dir(\\boldsymbol\\alpha)} \\\\\n",
    "\\boldsymbol{\\phi}_{k=1,\\ldots,K} &\\sim \\mathrm{Dir(\\boldsymbol\\beta)} \\\\\n",
    "z_{d=1,\\ldots,D,n=1,\\ldots,N_d} &\\sim \\mathrm{Cat}(\\boldsymbol{\\theta}_d) \\\\\n",
    "w_{d=1,\\ldots,D,n=1,\\ldots,N_d} &\\sim \\mathrm{Cat}(\\boldsymbol{\\phi}_{z_{dn}})\n",
    "\\end{align*}\n",
    "$$\n",
    "\n",
    "where \n",
    "\n",
    "- $\\boldsymbol\\theta_d$ is the topic distribution of document $d$. We assume that only a small number of topics have higher probability per document and therefore use a sparse Dirichlet prior for $\\boldsymbol\\theta_d$.  \n",
    "\n",
    "- $\\boldsymbol\\phi_k$ is the word distribution of topic $k$, as in the previous model.\n",
    "\n",
    "- $z_{dn}$ is the topic of word $w_{dn}$. It follows a categorical distribution that is parameterized by $\\boldsymbol\\theta_d$.\n",
    "\n",
    "- $w_{dn}$ is the $n$-th word of document $d$. It also follows a categorical distribution that is parameterized by $\\boldsymbol\\phi_{z_{dn}}$ i.e. the word distribution of topic $z_{dn}$.\n",
    "\n",
    "Model specification with PyMC3 again closely follows the formal definition:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = np.ones(K) * 0.3\n",
    "beta = np.ones(V) * 0.3\n",
    "\n",
    "with pm.Model() as model:\n",
    "    phi = pm.Dirichlet(\"phi\", a=beta, shape=(K, V))\n",
    "\n",
    "    for i in range(D):\n",
    "        theta = pm.Dirichlet(f\"theta_{i}\", a=alpha)\n",
    "        \n",
    "        z = pm.Categorical(f\"z_{i}\", p=theta, shape=X[i].shape)\n",
    "        w = pm.Categorical(f\"w_{i}\", p=phi[z], observed=X[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Variables $\\boldsymbol\\theta_d$ from the formal definition are now implemented inside the `for` loop as PyMC3 random variables `theta_0`, ..., `theta_12`, one for each document. \n",
    "\n",
    "- Variables $z_{dn}$ are implemented inside the `for` loop as PyMC3 variables `z_0`, ..., `z_12`. Their shape is derived from the number of words in the document. \n",
    "\n",
    "- The implementation of variables $\\boldsymbol\\phi_k$ and $w_{dn}$ is identical to the previous model.\n",
    "\n",
    "Again, we draw 2000 posterior samples via MCMC so that we can compute estimates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sequential sampling (1 chains in 1 job)\n",
      "CompoundStep\n",
      ">NUTS: [theta_12, theta_11, theta_10, theta_9, theta_8, theta_7, theta_6, theta_5, theta_4, theta_3, theta_2, theta_1, theta_0, phi]\n",
      ">CategoricalGibbsMetropolis: [z_12, z_11, z_10, z_9, z_8, z_7, z_6, z_5, z_4, z_3, z_2, z_1, z_0]\n",
      "100%|██████████| 2500/2500 [04:24<00:00, 10.07it/s]\n",
      "There were 129 divergences after tuning. Increase `target_accept` or reparameterize.\n",
      "Only one chain was sampled, this makes it impossible to run some convergence checks\n"
     ]
    }
   ],
   "source": [
    "with model:    \n",
    "    trace = pm.sample(2000, chains=1, nuts_kwargs={'target_accept': 0.9})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The density estimates for `phi` are similar to the previous example which is expected. Only the assignment of topic numbers may change between MCMC runs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_phi_estimates(trace)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can additionally analyze the topic distributions per document. In the following output we see that in documents 0-6 topic 1 has the highest probability, in documents 7-10 it is topic 0 and in documents 11-12 it is topic 2. In addition to the $\\boldsymbol\\theta_d$ mean values the 95% HPD interval is computed for the topic with the highest probability. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>$\\boldsymbol\\theta_{d,0}$</th>\n",
       "      <th>$\\boldsymbol\\theta_{d,1}$</th>\n",
       "      <th>$\\boldsymbol\\theta_{d,2}$</th>\n",
       "      <th>HPD 2.5</th>\n",
       "      <th>HPD 97.5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Document</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.056</td>\n",
       "      <td>0.871</td>\n",
       "      <td>0.072</td>\n",
       "      <td>0.534</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.063</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.603</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.077</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.086</td>\n",
       "      <td>0.447</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.057</td>\n",
       "      <td>0.879</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.599</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.133</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.067</td>\n",
       "      <td>0.460</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.171</td>\n",
       "      <td>0.734</td>\n",
       "      <td>0.094</td>\n",
       "      <td>0.307</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.056</td>\n",
       "      <td>0.885</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.630</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.630</td>\n",
       "      <td>0.193</td>\n",
       "      <td>0.177</td>\n",
       "      <td>0.091</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.853</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.092</td>\n",
       "      <td>0.426</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.533</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.331</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.782</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.169</td>\n",
       "      <td>0.331</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.151</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.799</td>\n",
       "      <td>0.366</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.065</td>\n",
       "      <td>0.119</td>\n",
       "      <td>0.816</td>\n",
       "      <td>0.475</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          $\\boldsymbol\\theta_{d,0}$  $\\boldsymbol\\theta_{d,1}$  \\\n",
       "Document                                                         \n",
       "0                             0.056                      0.871   \n",
       "1                             0.063                      0.877   \n",
       "2                             0.077                      0.838   \n",
       "3                             0.057                      0.879   \n",
       "4                             0.133                      0.800   \n",
       "5                             0.171                      0.734   \n",
       "6                             0.056                      0.885   \n",
       "7                             0.630                      0.193   \n",
       "8                             0.853                      0.055   \n",
       "9                             0.533                      0.136   \n",
       "10                            0.782                      0.049   \n",
       "11                            0.151                      0.050   \n",
       "12                            0.065                      0.119   \n",
       "\n",
       "          $\\boldsymbol\\theta_{d,2}$  HPD 2.5  HPD 97.5  \n",
       "Document                                                \n",
       "0                             0.072    0.534     1.000  \n",
       "1                             0.061    0.603     1.000  \n",
       "2                             0.086    0.447     1.000  \n",
       "3                             0.064    0.599     1.000  \n",
       "4                             0.067    0.460     1.000  \n",
       "5                             0.094    0.307     1.000  \n",
       "6                             0.060    0.630     1.000  \n",
       "7                             0.177    0.091     1.000  \n",
       "8                             0.092    0.426     1.000  \n",
       "9                             0.331    0.000     0.986  \n",
       "10                            0.169    0.331     1.000  \n",
       "11                            0.799    0.366     1.000  \n",
       "12                            0.816    0.475     1.000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "data = []\n",
    "\n",
    "for i in range(D):\n",
    "    row = np.mean(trace[f'theta_{i}'], axis=0).tolist()\n",
    "    row.extend(pm.stats.hpd(trace[f'theta_{i}'])[np.argmax(row)])\n",
    "    data.append(row)\n",
    "    \n",
    "pd.options.display.float_format = '{:,.3f}'.format    \n",
    "df = pd.DataFrame(data, columns=['$\\\\boldsymbol\\\\theta_{d,0}$', \n",
    "                                 '$\\\\boldsymbol\\\\theta_{d,1}$', \n",
    "                                 '$\\\\boldsymbol\\\\theta_{d,2}$', 'HPD 2.5', 'HPD 97.5'])\n",
    "df.index.name = 'Document'\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The topic distribution for document with index 9 is less skewed compared to other documents which makes sense since it contains words from all three topics. To obtain the topic for each word in that document we compute the posterior mode of `z_9`, again skipping the computation of uncertainty estimates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic of word \"statistics\" = 0\n",
      "Topic of word \"Python\" = 1\n",
      "Topic of word \"statistics\" = 0\n",
      "Topic of word \"statistics\" = 0\n",
      "Topic of word \"deep learning\" = 0\n",
      "Topic of word \"Postgres\" = 2\n"
     ]
    }
   ],
   "source": [
    "doc_idx = 9\n",
    "\n",
    "for i, w in enumerate(documents[doc_idx]):\n",
    "    topics, counts = np.unique(trace[f'z_{doc_idx}'][:, i], return_counts=True)\n",
    "    print(f'Topic of word \"{w}\" = {topics[np.argmax(counts)]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results make perfectly sense, topics have been correctly assigned to words. For more options to analyze MCMC traces you may want to take a look at the PyMC3 modules [stats](https://docs.pymc.io/api/stats.html) and [plots](https://docs.pymc.io/api/plots.html).\n",
    "\n",
    "It is quite easy to get started with topic modeling in PyMC3 but the implementation presented here is rather naive. It samples from the full posterior over all unknown variables which is quite inefficient. A more efficient implementation would sample from marginal posteriors as described in [this paper](https://www.semanticscholar.org/paper/Finding-scientific-topics.-Griffiths-Steyvers/e99f196cf21e0781ef1e119d14e6db45cd71bf3b), for example. Other approaches use variational inference instead of MCMC. A PyMC3 example based on ADVI is [here](https://docs.pymc.io/notebooks/lda-advi-aevb.html). An efficient implementation in [scikit-learn](https://scikit-learn.org/stable/index.html) is [LatentDirichletAllocation](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html) which is also based on a variational Bayes algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appendix\n",
    "\n",
    "### Code for plate notations\n",
    "\n",
    "[DAFT](http://daft-pgm.org/) is used for generating plate notation figures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import daft"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Naive Bayes model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 226.772x170.079 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pgm = daft.PGM([4, 3], origin=(-0.5, -1))\n",
    "pgm.add_node(daft.Node(\"alpha\", r\"$\\mathbf{\\alpha}$\", 0, 0))\n",
    "pgm.add_node(daft.Node(\"beta\", r\"$\\mathbf{\\beta}$\", 2, 1.5))\n",
    "pgm.add_node(daft.Node(\"theta\", r\"$\\mathbf{\\theta}$\", 1, 0))\n",
    "pgm.add_node(daft.Node(\"phi\", r\"$\\mathbf{\\phi}_k$\", 3, 1.5))\n",
    "pgm.add_node(daft.Node(\"z\", r\"$z_{d}$\", 2, 0))\n",
    "pgm.add_node(daft.Node(\"w\", r\"$w_{dn}$\", 3, 0, observed=True))\n",
    "pgm.add_edge(\"alpha\", \"theta\")\n",
    "pgm.add_edge(\"beta\", \"phi\")\n",
    "pgm.add_edge(\"theta\", \"z\")\n",
    "pgm.add_edge(\"phi\", \"w\")\n",
    "pgm.add_edge(\"z\", \"w\")\n",
    "pgm.add_plate(daft.Plate([2.4, 1.0, 1.0, 1.0], label=r\"$K$\"))\n",
    "pgm.add_plate(daft.Plate([1.4, -0.7, 2.0, 1.4], label=r\"$D$\"))\n",
    "pgm.add_plate(daft.Plate([2.4, -0.55, 0.95, 1.1], label=r\"$N_d$\"))\n",
    "ax = pgm.render()\n",
    "ax.set_title('Naive Bayes model');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Latent Dirichlet allocation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 226.772x170.079 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pgm = daft.PGM([4, 3], origin=(-0.5, -1))\n",
    "pgm.add_node(daft.Node(\"alpha\", r\"$\\mathbf{\\alpha}$\", 0, 0))\n",
    "pgm.add_node(daft.Node(\"beta\", r\"$\\mathbf{\\beta}$\", 2, 1.5))\n",
    "pgm.add_node(daft.Node(\"theta\", r\"$\\mathbf{\\theta}_d$\", 1, 0))\n",
    "pgm.add_node(daft.Node(\"phi\", r\"$\\mathbf{\\phi}_k$\", 3, 1.5))\n",
    "pgm.add_node(daft.Node(\"z\", r\"$z_{dn}$\", 2, 0))\n",
    "pgm.add_node(daft.Node(\"w\", r\"$w_{dn}$\", 3, 0, observed=True))\n",
    "pgm.add_edge(\"alpha\", \"theta\")\n",
    "pgm.add_edge(\"beta\", \"phi\")\n",
    "pgm.add_edge(\"theta\", \"z\")\n",
    "pgm.add_edge(\"phi\", \"w\")\n",
    "pgm.add_edge(\"z\", \"w\")\n",
    "pgm.add_plate(daft.Plate([2.4, 1.0, 1.0, 1.0], label=r\"$K$\"))\n",
    "pgm.add_plate(daft.Plate([0.4, -0.7, 3.0, 1.4], label=r\"$D$\"))\n",
    "pgm.add_plate(daft.Plate([1.4, -0.55, 1.95, 1.1], label=r\"$N_d$\"))\n",
    "ax = pgm.render()\n",
    "ax.set_title('Latent Dirichlet allocation');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
